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Bradley C. Naylor, Christian N. K. Anderson, Marcus Hadfield, David H. Parkinson, Austin Ahlstrom, Austin Hannemann, Chad R. Quilling, Kyle J. Cutler, Russell L. Denton, Robert Adamson, Thomas E. Angel, Rebecca S. Burlett, Paul S. Hafen, John. C. Dallon, Mark K. Transtrum, Robert D. Hyldahl, and John C. Price.
"Utilizing Nonequilibrium Isotope Enrichments to Dramatically Increase Turnover Measurement Ranges in Single Biopsy Samples from Humans" (Sep 2022).
Journal of Proteome Research 2022 21 (11), 2703-2714.
DOI: 10.1021/acs.jproteome.2c00380.
Link: https://pubs.acs.org/doi/10.1021/acs.jproteome.2c00380 |
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Applied Mathematics, Biochemistry |
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Liu, Y.H., Smith, S., Mihalas S., Shea-Brown E., Sumbul U., “Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators”, Advances in Neural Information Processing Systems, 2022. |
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Applied Mathematics, Computer Science, Machine Learning, Mathematical Neuroscience |
Liu, Y.H., Ghosh A., Richards B. A., Shea-Brown E., Lajoie G., “Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules”, Advances in Neural Information Processing Systems, 2022. |
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Applied Linear Algebra, Applied Mathematics, Computer Science, Dynamical Systems, Machine Learning, Mathematical Neuroscience |
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Tim Leung. (2021) Employee Stock Options, Exercise Timing, Hedging, and Valuation. World Scientific |
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Financial Mathematics, Risk Management, Stochastic Modeling |
Maass, K., Aravkin, A., & Kim, M. (2021). A feasibility study of a hyperparameter tuning approach to automated inverse planning in radiotherapy. arXiv preprint arXiv: arXiv:2105.07024. |
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Applied Mathematics, Cancer Modeling, Machine Learning, Optimization and Variational Analysis |
Yang, Y.-J. and Cheng, Y.-C., "Potentials of continuous Markov processes and random perturbations" J. Phys. A: Math. Theor. 54 195001 (2021) |
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Dynamical Systems, Mathematical Physics, Probability, Stochastic Modeling |
Yang, Y.-J., & Qian, H. Bivectorial Nonequilibrium Thermodynamics: Cycle Affinity, Vorticity Potential, and Onsager’s Principle. J Stat. Phys. 182, 46 (2021). |
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Biophysics, Dynamical Systems, Mathematical Biology, Mathematical Physics, Probability, Stochastic Modeling |
Liu, Y.H., Smith, S.J., Mihalas S., Shea-Brown E., Sumbul U., “Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network”, Proceedings of the National Academy of Sciences, 2021. |
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Computer Science, Machine Learning, Mathematical Neuroscience |
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Liu, Y.H., Smith, S.J., Mihalas S., Shea-Brown E., Sumbul U., “A solution to temporal credit assignment using cell-type-specific modulatory signals”, bioRxiv, 2020. |
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Machine Learning, Mathematical Neuroscience |
Yang, Y.-J. & Qian, H. Unified formalism for entropy production and fluctuation relations. Phys. Rev. E 101 , 022129 (2020) |
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Biophysics, Dynamical Systems, Mathematical Biology, Mathematical Physics, Probability, Stochastic Modeling |
Maass, K., Kim, M., & Aravkin, A. (2020). A nonconvex optimization approach to IMRT planning with dose-volume constraints. arXiv preprint arXiv:1907.10712. |
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Applied Mathematics, Cancer Modeling, Inverse Problems, Optimization and Variational Analysis |